f_cum_dose

Obtains the cumulative dose given treatment duration and
dosing schedule.

Performs drug demand forecasting by modeling drug dispensing data while taking into account predicted enrollment and treatment discontinuation dates. The gap time between randomization and the first drug dispensing visit is modeled using interval-censored exponential, Weibull, log-logistic, or log-normal distributions (Anderson-Bergman (2017) <doi:10.18637/jss.v081.i12>). The number of skipped visits is modeled using Poisson, zero-inflated Poisson, or negative binomial distributions (Zeileis, Kleiber & Jackman (2008) <doi:10.18637/jss.v027.i08>). The gap time between two consecutive drug dispensing visits given the number of skipped visits is modeled using linear regression based on least squares or least absolute deviations (Birkes & Dodge (1993, ISBN:0-471-56881-3)). The number of dispensed doses is modeled using linear or linear mixed-effects models (McCulloch & Searle (2001, ISBN:0-471-19364-X)).

Kaifeng Lu

drugDemand

Drug Demand Forecasting

f_cum_dose function

<dl><dt>x</dt>
<dd>Treatment duration.</dd>
<dt>w</dt>
<dd>Number of days per treatment cycle.</dd>
<dt>d</dt>
<dd>Dose per treatment cycle.</dd>
<dt>N</dt>
<dd>Maximum number of treatment cycles.</dd></dl>

Arguments

Kaifeng Lu, <a href="/link/kaifenglu%40gmail.com?package=drugDemand&version=0.1.3" data-mini-rdoc="drugDemand::kaifenglu@gmail.com">kaifenglu@gmail.com</a>

Author

Cumulative Dose — f_cum_dose

<dl>

<dt>x</dt>
<dd>Treatment duration.</dd>


<dt>w</dt>
<dd>Number of days per treatment cycle.</dd>


<dt>d</dt>
<dd>Dose per treatment cycle.</dd>


<dt>N</dt>
<dd>Maximum number of treatment cycles.</dd>

</dl>

Kaifeng Lu, <a href='mailto:kaifenglu@gmail.com'>kaifenglu@gmail.com</a>

f_cum_dose: Cumulative Dose

Description

Usage

Value

Arguments

Author

Examples